autograd

Inference for agent systems

Autograd every run.

Route every agent call through Qwen 3.8 27B or Kimi K3. Connect once. Autograd turns each inference rollout and its outcome into a learning signal for the next model.

Try the models Read the quickstart
The Autograd reinforcement learning loop An agent request moves through inference to an outcome and reward, then a gradient update produces the next model version. AGENT INFERENCE OUTCOME REWARD ∂L / ∂θ forward pass backward pass
01
01The reinforcement loop

The run is
the training data.

Autograd joins inference with the outcomes that happen after inference. The result is a controlled path from production behavior to reinforcement learning.

  1. 1

    Run

    Serve the agent through Autograd inference. Capture model steps, tools, latency, usage, and model version.

  2. 2

    Reward

    Join product outcomes, user corrections, graders, or explicit scores to the run.

  3. 3

    Reinforce

    Evaluate and train against the work your agent actually performs—not a generic benchmark alone.

  4. 4

    Promote

    Compare the candidate with the baseline and deliberately promote a better checkpoint.

02Ownership

Own Your
Intelligence.

Your agent improves from the work only your organization can see. Autograd turns those runs into model intelligence you control—from the signals that shape it to the checkpoint you deploy.

Your context Your rewards Your model